class_docstr: LSTM Model
  
  Parameters
  ----------
  d_feat : int
      input dimension for each time step
  metric: str
      the evaluation metric used in early stop
  optimizer : str
      optimizer name
  GPU : str
      the GPU ID(s) used for training
code: def train_epoch(self, data_loader):
      self.LSTM_model.train()
  
      for data, weight in data_loader:
          feature = data[:, :, 0:-1].to(self.device)
          label = data[:, -1, -1].to(self.device)
  
          pred = self.LSTM_model(feature.float())
          loss = self.loss_fn(pred, label, weight.to(self.device))
  
          self.train_optimizer.zero_grad()
          loss.backward()
          torch.nn.utils.clip_grad_value_(
              self.LSTM_model.parameters(), 3.0
          )
          self.train_optimizer.step()
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset.handler import DataHandlerLP
  from ...model.utils import ConcatDataset
  from ...data.dataset.weight import Reweighter
label_desc: Use image node_id 6 for calling the LSTM obj's underlying member method code with example usage: obj.train_epoch(data_loader) without return types